b939c910d036d8197ac7c63e5f188bc4c4fc4803,foolbox/batching.py,,run_parallel,#Any#Any#Any#Any#Any#Any#Any#Any#,94

Before Change

            logging.debug("calling native forward with {}".format(len(attacks_requesting_batched_predictions)))  // noqa: E501
            batched_predictions_args = list(map(np.stack,
                                                zip(*batched_predictions_args)))
            batched_predictions_args = list(batched_predictions_args)
            // get original shape (//attacks, batch size)
            batch_shape = batched_predictions_args[0].shape
            // merge individual batches into one super-batch
            batched_predictions_args[0] = batched_predictions_args[0].reshape(
                -1, *batch_shape[2:])

            batched_predictions = model.forward(*batched_predictions_args)
            // split super-batch back into individual batches
            batched_predictions = batched_predictions.reshape(
                *batch_shape[:2], -1)
        else:
            batched_predictions = []

        if len(attacks_requesting_gradients) > 0:

After Change

            // merge individual batches into one larger super-batch
            batch_lengths = [len(x) for x in inputs]
            batch_splits = np.cumsum(batch_lengths)
            inputs = np.concatenate([x for x in inputs])

            // split super-batch back into individual batches
            batched_predictions = model.forward(inputs)
            batched_predictions = np.split(batched_predictions, batch_splits,
                                           axis=0)

        else:
            batched_predictions = []

Instances